title: Impact of risk assessments on prophylactic antiemetic prescription and the incidence of postoperative nausea and vomiting: a cluster-randomized trial.
abstract: BACKGROUND
  Clinical prediction models have been shown to have moderate sensitivity and specificity, yet their use will depend on implementation in clinical practice. The authors hypothesized that implementation of a prediction model for postoperative nausea and vomiting (PONV) would lower the PONV incidence by stimulating anesthesiologists to administer more "risk-tailored" prophylaxis to patients.
  
  
  METHODS
  A single-center, cluster-randomized trial was performed in 12,032 elective surgical patients receiving anesthesia from 79 anesthesiologists. Anesthesiologists were randomized to either exposure or nonexposure to automated risk calculations for PONV (without patient-specific recommendations on prophylactic antiemetics). Anesthesiologists who treated less than 50 enrolled patients were excluded during the analysis to avoid too small clusters, yielding 11,613 patients and 57 anesthesiologists (intervention group: 5,471 and 31; care-as-usual group: 6,142 and 26). The 24-h incidence of PONV (primary outcome) and the number of prophylactic antiemetics administered per patient were studied for risk-dependent differences between allocation groups.
  
  
  RESULTS
  There were no differences in PONV incidence between allocation groups (crude incidence intervention group 41%, care-as-usual group 43%; odds ratio, 0.97; 95% CI, 0.87-1.1; risk-dependent odds ratio, 0.92; 95% CI, 0.80-1.1). Nevertheless, intervention-group anesthesiologists administered more prophylactic antiemetics (rate ratio, 2.0; 95% CI, 1.6-2.4) and more risk-tailored than care-as-usual-group anesthesiologists (risk-dependent rate ratio, 1.6; 95% CI, 1.3-2.0).
  
  
  CONCLUSIONS
  Implementation of a PONV prediction model did not reduce the PONV incidence despite increased antiemetic prescription in high-risk patients by anesthesiologists. Before implementing prediction models into clinical practice, implementation studies that include patient outcomes as an endpoint are needed.
authors.affiliation: From the Department of Anesthesiology, Division of Anesthesiology, Intensive Care and Emergency Medicine (T.H.K., L.v.W., C.J.K., W.A.v.K., K.G.M.M.) and Julius Center for Health Sciences and Primary Care (K.G.M.M., Y.V.), University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Teus H
authors.initials: TH
authors.lastname: Kappen
authors.forename: Karel G M
authors.initials: KG
authors.lastname: Moons
authors.forename: Leo
authors.initials: L
authors.lastname: van Wolfswinkel
authors.forename: Cornelis J
authors.initials: CJ
authors.lastname: Kalkman
authors.forename: Yvonne
authors.initials: Y
authors.lastname: Vergouwe
authors.forename: Wilton A
authors.initials: WA
authors.lastname: van Klei
mesh_terms: D000328:Adult; D000368:Aged; D000932:Antiemetics / Q000627:therapeutic use*; D003627:Data Interpretation, Statistical; D004311:Double-Blind Method; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016017:Odds Ratio; D020250:Postoperative Nausea and Vomiting / Q000175:diagnosis* / Q000517:prevention & control*; D011379:Prognosis; D012044:Regression Analysis; D018570:Risk Assessment; D012307:Risk Factors; D016896:Treatment Outcome
chemical_list: D000932:Antiemetics